Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 46
... shown that an optimum classifying machine could be achieved by computing and comparing the lx ( i ) . The computations are particularly simple if the loss function ( ij ) is assumed to be of the type λ ( ij ) = 1 - Sij ( 3.5 ) i = where ...
... shown that an optimum classifying machine could be achieved by computing and comparing the lx ( i ) . The computations are particularly simple if the loss function ( ij ) is assumed to be of the type λ ( ij ) = 1 - Sij ( 3.5 ) i = where ...
Sivu 103
... shown in Fig . 6.5 . If it were made too long , it would never be among the weight vectors closest to the hyperplanes which it must eventually cross . Therefore , it would never be adjusted , and W1 and W3 would wander around ...
... shown in Fig . 6.5 . If it were made too long , it would never be among the weight vectors closest to the hyperplanes which it must eventually cross . Therefore , it would never be adjusted , and W1 and W3 would wander around ...
Sivu 108
... shown in Fig . 6.8 . Note that the partition shown in Fig . 6 · 7a is also nonredundant . A nonredundant partition is not necessarily one that uses a minimum number of hyperplanes , however . Thus in Fig . 6 · 8a , one hyperplane ( line ) ...
... shown in Fig . 6.8 . Note that the partition shown in Fig . 6 · 7a is also nonredundant . A nonredundant partition is not necessarily one that uses a minimum number of hyperplanes , however . Thus in Fig . 6 · 8a , one hyperplane ( line ) ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |